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Resumen Objetivo: Este trabajo analiza el costo óptimo de expansión, el número de líneas a incluir y el tiempo de simulación computacional para dos sistemas transmisión, empleando programación no lineal entera mixta a través de los solver del software GAMS (General Algebraic Modeling System). El objetivo es determinar las diferencias en los costos de expansión cuando se emplea el modelo de transporte, DC, híbrido lineal y lineal disyuntivo. Metodología: Está dividida en cinco etapas: la primera identifica el sistema de transmisión, la segunda establece el problema de planeamiento del sistema de transmisión, la tercera realiza la formulación del sistema de potencia de acuerdo con cada uno de los modelos de optimización, la cuarta aplica la formulación del sistema de potencia en el software GAMS y la última selecciona el costo óptimo. Resultados: Desde el análisis de costo óptimo, el modelo DC y el modelo lineal disyuntivo presentaron el mayor costo óptimo respecto al modelo de transporte y al modelo hibrido lineal. Conclusiones: Los modelos implementados presentaron desempeños equivalentes en el sistema de prueba con el menor número de nodos; al aumentar el número de nodos, el desempeño de los modelos no presenta similitud. Por el contrario, se identifican diferencias importantes en los resultados obtenidos, lo que permite caracterizar solver específicos de acuerdo con el número de nodos. Abstract Objective: This work analyzes the optimal expansion cost, the number of lines to include, and the computational simulation time for two transmission systems, using mixed-integer nonlinear programming through the solvers of the GAMS (General Algebraic Modeling System) software. The objective is to determine the differences in expansion costs using the transport model, DC model, linear hybrid, and disjunctive linear. Methodology: The methodology of this study is divided into five stages. The first identifies the transmission system; the second establishes the transmission system planning problem; the third performs the formulation of the power system according to each of the optimization models; the fourth applies the formulation of the power system in the GAMS software; the last selects the optimal cost. Results: Considering the optimal cost analysis, the DC model and the disjunctive linear model present the highest optimal cost concerning the transport model and the linear hybrid model. Conclusions: The implemented models presented equivalent performances in the test system with the smallest number of nodes; by increasing the number of nodes, the performance of the models does not present similarity. On the contrary, the results present significant differences that allow characterizing specific solvers according to the number of nodes.

With the penetration of renewable energy sources based distributed generation (RES-DG) in the distribution network (DN), the operating time of protection relays becomes a major concern to avoid the mis-coordination and nuisance tripping of RES-DG. It is due to the bidirectional flow of current, variance in the fault current level, i.e. high fault current in case of Squirrel cage induction generator (SCIG) based RES-DG, and low fault current for an inverter-based RES-DGs. This paper proposes a novel User-defined dual setting direction overcurrent relay with hybrid time current-voltage (UDDOR-TCV) characteristics to intensify the proposed scheme’s flexibility without any communication assistance for radial DN. The proposed model is formulated in a constrained non-linear optimization fashion and solved with the MINLP solver of general algebraic modelling system(GAMS) software to determine the optimal relay settings. The propounded protection scheme is simulated on IEEE-33 bus radial distribution system and a local 40-bus system hosting the multiple SCIG and PV based RES-DGs at optimal locations. Detailed numerical studies are carried out to show the performance of the proposed scheme. 66.848% and 68.26% reduction in relay operation time with zero mis-coordination is achieved with SCIG-WTG case and PV-units case for the IEEE-33 bus system and a similar reduction in operation time is 68.937% and 66.563% for Local-40 bus system. Moreover, the relays numbers are reduced by 32.692% and 39.7% for IEEE-33 and Local-40 bus systems, respectively, due to the forward/reverse characteristics capability of the proposed UDDOR-TCV. The performance of the proposed scheme is also evaluated on IEEE-8 bus meshed DN. The results are compared with the existing protection schemes in recent literature.

This paper presents the economic analysis and optimal energy management of a grid-connected MG that comprises renewable energy resources and different battery storage technologies with different characteristics such as initial charge, depth of discharge, and the number of charging/discharging cycles to minimize the total operating cost of the system by maximizing the benefits of BSS, minimizing the investment and replacement cost of BSS, and minimizing the operation and maintenance cost of DGs. Several constraints are considered, such as the output power limits of the distributed generators, the limits of power imported from or exported to the grid, load balance, and other sets of battery storage constraints. The general algebraic modeling system (GAMS) is used to solve the deterministic optimization problem. Second, stochastic optimization is used to solve the deterministic problem with market price uncertainty. Third, robust optimization using the information gap decision theory is presented to model the electric load uncertainty. The validity and effectiveness of the proposed solution are explained by comparing the results obtained by GAMS to the results obtained by other optimization techniques presented in the literature.

In this article, we present a new open-source tool for algebraic modeling and mathematical optimization. We begin by distilling the main gaps within the existing algebraic modeling languages and tools (varying performance, limited cross-compatibility, complex syntax, and different solver, feature, and problem type support). Later, we propose a state-of-the-art web-based tool (WebAML and Optimization System) for algebraic modeling languages and mathematical optimization. The tool does not require specific algebraic language knowledge, allows solving problems using different solvers, and utilizes the best characteristics of existing algebraic modeling languages. We also provide clear extension points and ideas on how we could further improve such a tool.

DIETER is an open-source power sector model designed to analyze future settings with very high shares of variable renewable energy sources. It minimizes overall system costs, including fixed and variable costs of various generation, flexibility and sector coupling options. Here we introduce DIETERpy that builds on the existing model version, written in the General Algebraic Modeling System (GAMS), and enhances it with a Python framework. This combines the flexibility of Python regarding pre- and post-processing of data with a straightforward algebraic formulation in GAMS and the use of efficient solvers. DIETERpy also offers a browser-based graphical user interface. The new framework is designed to be easily accessible as it enables users to run the model, alter its configuration, and define numerous scenarios without a deeper knowledge of GAMS. Code, data, and manuals are available in public repositories under permissive licenses for transparency and reproducibility.

The optimum route for garbage transport vehicles is restricted by vehicle capacity and time windows that the garbage transport vehicle starts at the origin and does not return to the origin. The problem of transporting waste routes is a robust optimization problem where the amount of waste in an area and travel time is uncertain. In the real world, traffic jams and vehicle engine damage can cause delays. This paper proposes the robust counterpart open capacitated vehicle routing problem (denoted by RCOCVRP) with soft time windows model. The aim of RCOCVRP with soft time windows model is to find schedule and optimum route of transporting waste. This model calculation uses LINGO software and GAMS software. Finally for the evaluation of the RCOCVRP model with soft time windows on the proposed waste transportation problem is conducted so that it hasa feasible solution.

The renewable sources have made an impact on economic benefits and the electrical quality of the distribution system. Therefore, a multi-scenario optimization model is proposed, targeting to optimize the investment of renewable sources considering uncertainties based on the minimum life cycle cost. The mathematic model allows selecting the siting, sizing and type of renewable sources in distribution system. The objective function is minimizing life-cycle cost of the project during the planning period, including the investment and operation cost of renewable sources, the cost of purchasing energy from the grid, the emission taxes and residual value of the equipment at the end of the planning period. The nonlinear power flow model alternating current is utilized to concurrently balance both active and reactive power at each state as well as the constraints for the limit capacity of feeders. Connectable substation and the constraints in selecting the renewable sources are also represented and thus improving the accuracy of the calculated results of the distribution system. The uncertainty parameters of renewable sources (photovoltaic and wind turbine), electricity price and load were modeled by the probability density functions and are considered in the optimization model. The clustering technique was utilized to divide each parameter into states then all states of parameters are integrated by the combined model. The general algebraic modeling system (GAMS) was applied to solve the optimization problem with the test system and demonstrated the advantages of the proposed model.

Nowadays, people are increasingly interested in renewable energy sources and accumulation of energy for its efficient use. The use of non-renewable resources is progressively decreasing due to their adverse changes in climate conditions and high production of CO2 emissions. This work deals with the problem of heat accumulation by means of the phase change of a material using the Stefan problem, which serves to describe the temperature distribution in the medium and to determine the location of the interface between the solid and liquid phase. This approach is used to determine desired properties and thermal behaviour of the material under different accumulation requirements. The main objective was to create and solve an optimization model in order to determine heat transfer conditions and other parameters to ensure the extrema of thermal behaviour characterization.

This paper proposes a novel energy hub model for areas using both heat and cold demands that arise due to the major changes in environmental temperature in different periods of the year. The energy demand and the electrical price in a competitive electricity market are uncertain with stochastic values which are usually performed by a probability distribution function. Therefore, a stochastic mathematical model representing an optimal operation of energy hub is based on the objective function of minimization of energy costs (including electricity and gas). Several constraints such as energy balance, limited capacity of the transformer, air conditioners, gas boilers, absorption chillers, combined heat, and power and battery energy storage system are also incorporated into the model to guarantee the required specifications. The high-level algebraic modeling software, general algebraic modeling system has been employed to undertake calculations. Finally, numerical results have illustrated the efficiency and capability of the proposed models.

Immunotherapies are becoming a promising strategy for malignant disease. Selectively directing host immune responses to target cancerous tissue is a milestone of human health care. The roles of the innate and adaptive immune systems in both cancer progression and elimination are now being realized. Defining the immune cell environment and identifying the contributions of each sub-population of these cells has lead to an understanding of the immunotherapeutic processes, and demonstrated the potential of the immune system to drive cancer shrinkage and sustained immunity against disease. Poorly treated diseases, such as high-grade glioma, suffer from lack of therapeutic efficacy and rapid progression. Immunotherapeutic success in other solid malignancies, such as melanoma, now provides the principals for which this treatment paradigm can be adapted for primary brain cancers. The central nervous system is complex, and relative contributions of immune sub-populations to high grade glioma progression are not fully characterized. Here, we summarize recent research in both animal and humans which add to the knowledge base of how innate and adaptive immune cells contribute to glioma progression, and outline work which has demonstrated their potential to elicit anti-tumorigenic responses. Additionally, we highlight Neuropilin 1, a cell surface receptor protein, describe its signaling functions in the context of immunity, and point to its potential to slow glioma progression.

The optimization of a multi-generation system which represents the integrated dual-purpose desalination plant and a low-scale absorption refrigeration system is addressed. A nonlinear mathematical programming optimization model that integrates a natural gas combined-cycle, a multi-effect distillation desalination plant, a series flow double-effect water-lithium bromide absorption refrigeration system, and a water heater, is developed based on first-principle models. The model is implemented in General Algebraic Modelling System and a generalized gradient-based optimization algorithm is used. Given design specifications for electricity generation (around 37 MW), freshwater production (100 kg/s), refrigeration capacity (2 MW), and thermal load for heating (around 0.7 MW of hot water), the integrated system is optimized by minimizing two objective functions by single-objective optimization: total heat transfer area and total annual cost. As a result, minimum total heat transfer area values of 39148 m2, 36002 m2, and 35161 m2 are obtained when 4, 5, and 6 distillation effects were considered in the multi-effect distillation system, respectively. Also, a minimum annual cost of around 24 MM$/yr. is obtained for 5 distillation effects. The influence of the number of effects in the multi-effect distillation subsystem on the optimal solutions is analyzed. Cost-effective optimal solutions are developed for the studied multi-generation system. Fil: Pietrasanta, Ariana Milagros. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Morosuk, Tatiana. Technishe Universitat Berlin; Alemania Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina

This paper applies mathematical programming and superstructure-based optimization approaches to design multi-stage membrane systems for CO2 capture, with the aim to systematically determine the optimal number of membrane stages, membrane areas, power requirements, the location of recycle streams, and operating conditions that satisfy desired CO2 recovery−purity value pairs ranging from 90 to 98% at minimum total cost. A superstructure that embeds several candidate configurations is represented as a NLP model which is used to investigate how these ranges of target values affect the optimal number of stages, membrane areas, operating conditions, and total cost. The results indicate that the optimal number of stages depend strongly on the desired CO2 purity. For CO2 purity ranging from 90 to 93%, the optimal configuration involves two stages and one recycle stream; from 94 to 96%, the optimal configuration involves three stages and two recycle streams; and finally, from 97 and 98%, the optimal configuration involves all the four stages proposed in the superstructure keeping the two recycle streams. For the same design specifications, the obtained process configurations compete not only with reference cases of multi-stage membrane systems in terms of total cost, but also with absorption-based CO2 capture processes in terms of energy requirements. The financial support from the Facultad Regional Rosario of the Universidad Tecnológica Nacional (FRRo) and the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) of Argentina are gratefully acknowledged.

The exploitation of combined photovoltaic (PV) plants and storage systems is nowadays assuming growing importance, due to the technical, environmental, and economical benefits which can derive from an optimal integration. In this paper, a mixed-integer algorithm for the optimal dispatch of a storage system, based on the day-ahead PV forecasting is developed. The optimization objective is the maximization of the total production of the integrated system, according to a requested active power profile, which can be defined by the operator. The study case of an existing distribution management system, which operates on the low-voltage microgrid at University of Genova is analyzed. The procedure is validated by field results with particular attention to the storage round-trip efficiency.

This paper fits into the process system engineering field by addressing the optimization of a two-stage membrane system for H2 separation in refinery processes. To this end, a nonlinear mathematical programming (NLP) model is developed to simultaneously optimize the size of each membrane stage (membrane area, heat transfer area, and installed power for compressors and vacuum pumps) and operating conditions (flow rates, pressures, temperatures, and compositions) to achieve desired target levels of H2 product purity and H2 recovery at a minimum total annual cost. Optimal configuration and process design are obtained from a model which embeds different operating modes and process configurations. For instance, the following candidate ways to create the driving force across the membrane are embedded: (a) compression of both feed and/or permeate streams, or (b) vacuum application in permeate streams, or (c) a combination of (a) and (b). In addition, the potential selection of an expansion turbine to recover energy from the retentate stream (energy recovery system) is also embedded. For a H2 product purity of 0.90 and H2 recovery of 90%, a minimum total annual cost of 1.764 M$&middot, year&minus, 1 was obtained for treating 100 kmol&middot, h&minus, 1 with 0.18, 0.16, 0.62, and 0.04 mole fraction of H2, CO, N2, CO2, respectively. The optimal solution selected a combination of compression and vacuum to create the driving force and removed the expansion turbine. Afterwards, this optimal solution was compared in terms of costs, process-unit sizes, and operating conditions to the following two sub-optimal solutions: (i) no vacuum in permeate stream is applied, and (ii) the expansion turbine is included into the process. The comparison showed that the latter (ii) has the highest total annual cost (TAC) value, which is around 7% higher than the former (i) and 24% higher than the found optimal solution. Finally, a sensitivity analysis to investigate the influence of the desired H2 product purity and H2 recovery is presented. Opposite cost-based trade-offs between total membrane area and total electric power were observed with the variations of these two model parameters. This paper contributes a valuable decision-support tool in the process system engineering field for designing, simulating, and optimizing membrane-based systems for H2 separation in a particular industrial case, and the presented optimization results provide useful guidelines to assist in selecting the optimal configuration and operating mode.

This paper deals with the optimization of the coupling between a natural gas combined cycle (NGCC) plant and a post-combustion CO2 capture process by minimizing the mitigation cost – defined as the ratio between the cost of electric power generation and the amount of CO2 emitted per unit of total net electric power generated – while satisfying the design specifications: electric power generation capacity and CO2 capture level. Three candidate coupling configurations, which differ in the place where the steam is extracted from, are optimized using detailed and rigorous models for both the NGCC and the CO2 capture plants. By comparing the mitigation cost of each configuration, the optimal integration configuration and the corresponding optimal sizes and operating conditions of all process units (steam turbines, gas turbines, heat recovery steam generators HRSGs, absorption and regeneration columns, reboilers and condensers, and pumps) are provided. In the computed optimal solution, the steam required by the CO2 capture plant is extracted from both the steam turbine and the HRSG (evaporator operating at low pressure), and the mitigation cost is 90.88 $/t CO2. The optimal solution is compared with suboptimal solutions corresponding to the other two candidate coupling schemes. These solutions are compared in detail regarding capital investment and operating costs, HRSG configuration, process unit sizes, and operating conditions. Fil: Mores, Patricia Liliana. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina Fil: Manassaldi, Juan Ignacio. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina Fil: Scenna, Nicolas Jose. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina Fil: Caballero, José A.. Universidad de Alicante; España Fil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Mussati, Sergio Fabian. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina

Although malaria burden has declined globally following scale up of intervention, the disease has remained a leading cause of hospitalization and deaths among children aged under-5 years in Nigeria. Malaria is known to be related to climate and environmental conditions. Previous research has usually studied the effects of these factors, neglecting possible correlation between them, high correlation among variables is a source of multicollinearity that induces overfitting in regression modelling. In this paper, a factor analysis was first introduced to circumvent the issue of multicollinearity and a Generalized Additive Model (GAM) was subsequently explored to identify the important risk factors that might influence the prevalence of childhood malaria in Nigeria. The GAM incorporated the complexity of the survey data, while simultaneously modelling the nonlinear and spatial random effects to allow a more precise identification of the major malaria risk factors that influence the geographical distribution of the disease. From our findings, the three latent factor components (constituted by humidity, precipitation, potential evapotranspiration, and wet days/maximum and minimum temperature/proximity to permanent waters, respectively) were significantly associated with malaria prevalence. Our analysis also detected statistically significant and nonlinear effect of altitude: the risk of malaria increased with lower values but declined sharply with higher values. A significant spatial variability in under-5 malaria prevalence across the survey clusters was also observed; malaria burden was higher in the northern part of Nigeria. Investigating the impact of important risk factors and geographical location on childhood malaria is of high relevance for the sustainable development goals (SDGs) 2015–2030 Agenda on malaria eradication, and we believe that the information obtained from this study and the generated risk maps can be useful to effectively target intervention efforts to high-risk areas based on climate and environmental context.

The exploitation of combined photovoltaic plants and storage systems is nowadays assuming growing importance due to the technical, energetic, environmental and economical benefits which can derive from an optimal integration. In this paper a mixed-integer algorithm for optimal dispatch of a lithium-ions storage system, based on the day ahead PV forecasting, is developed within an existing Distribution Management System, which operates on the low voltage Microgrid at University of Genova. The optimization object is the maximization of the Microgrid production according to the requested active power profile, which can be defined by the operator. The procedure has been validated by field results with particular attention to the storage efficiency, that has been modeled with additional constraints.

27th European Symposium on Computer Aided Process Engineering (ESCAPE 27), Barcelona, 1st-5th October, 2017. In this work, we analyze the effect of shale gas well data uncertainty on the multiobjective optimization of a multistage direct contact membrane distillation (DCMD) model. The uncertain parameters, flowrate and salt concentration of the flowback water, are modelled by a set of correlated scenarios. A bi-criterion stochastic MINLP was formulated to minimize the expected total annual cost (TAC) and its variability, controlled by the worst case (WC) risk management metric. The model was solved using a modified version of the sample average approximation (SAA) algorithm, which decomposes the original problem into two: a deterministic MINLP model and a stochastic NLP model. The solution is a set of Pareto curves, where the two global extreme solutions provide the DCMD designs that achieve the minimum expected TAC and the minimum WC, respectively. Furthermore, both designs are able to satisfy the zero liquid discharge (ZLD) requirement imposed in the outflow stream. European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 640979.

Water consumption required during the leaching stage in the surimi manufacturing process strongly depends on the design and the number and size of stages connected in series for the soluble protein extraction target, and it is considered as the main contributor to the operating costs. Therefore, the optimal synthesis and design of the leaching stage is essential to minimize the total annual cost. In this study, a mathematical optimization model for the optimal design of the leaching operation is presented. Precisely, a detailed Mixed Integer Nonlinear Programming (MINLP) model including operating and geometric constraints was developed based on our previous optimization model (NLP model). Aspects about quality, water consumption and main operating parameters were considered. The minimization of total annual costs, which considered a trade-off between investment and operating costs, led to an optimal solution with lesser number of stages (2 instead of 3 stages) and higher volumes of the leaching tanks comparing with previous results. An analysis was performed in order to investigate how the optimal solution was influenced by the variations of the unitary cost of fresh water, waste treatment and capital investment. Fil: Reinheimer, Maria Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina Fil: Scenna, Nicolas Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina Fil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina

Geodesic acoustic modes (GAMs) have been investigated in JET ohmic discharges using mainly Doppler backscattering. Characteristics and scaling properties of the GAM are studied. Time and spatial resolved measurements of the perpendicular velocity indicate that GAMs are located in a narrow layer at the edge density gradient region with amplitude corresponding to about 50% of the mean local perpendicular velocity. GAMs on JET appear to be regulated by the turbulence drive rather than by their damping rate. It is also shown that the GAM amplitude is ~20% larger in deuterium than in hydrogen plasmas. EURATOM 633053 Fundação para a Ciência e Tecnologia UID/FIS/50010/2013

Fugitive emissions are one of the pollution that can affect environment, health and also economy. It is a deemed to be an important part of the debate on climate change because they represent a significant portion of greenhouse gas emission. It is estimated that 60-70% of the fugitive emission comes from valve. This paper presented a study which focusing on fugitive emission by valve, which will be solved using GAMS to optimize the selection of valve and achieve the objective. The fugitive emission is minimized by replacing the current valve with new valve based on its emission rate. Type of valve that has been considered in this study is ball valve, gate valve, butterfly valve, globe valve and plug valve.Based from the results, it can be said that plug valve, gate valve and butterfly valve is most preferable compare to other type of valve for fugitive emission reduction. Different types of valves give different price, so, the selection of valve are depending on the budget of the project. The fugitive emission can be reducing in easy way at the design stage by using GAMS application to optimize it.

Understanding the persistence of fish aggregations is essential for the development of spatial fisheries measures and marine spatial planning advice. This study examined the temporal persistence of haddock Melanogrammus aeglefinus aggregations and life-stage sensitivity to environmental conditions in the North Sea during the period 1991 to 1997, using a 2-step generalized additive model. Geostatistical aggregation curves and the spaces selectivity index indicated that physical habitat preference was independent of changes in annual abundance. Juvenile haddock distribution did not conform to definitions of nursery area as there was no major shift in habitat use following settlement or seasonally persistent aggregation. Nevertheless, there were some significant age-related differences in temperature, depth, salinity and sediment preference. Depth explained the largest deviance in abundance models for most age-classes and seasons, but it was difficult to disentangle the relative contribution of this factor and salinity to habitat preference. The density of age 1 haddock indicated a preference for coarse sands in the winter and spring, which may reflect the distribution of prey. Thermal regime was an important constraint on age 0 distribution in quarter 3 (summer), as this life stage preferred areas, Sí

This paper considers optimisation of pump and valve schedules in complex large-scale water distribution networks (WDN). An optimisation model is automatically generated in GAMS language from a hydraulic model in EPANET format and from additional files describing operational constraints, electricity tariffs and pump station configurations. The paper describes how each hydraulic component is modelled. To reduce the size of the optimisation problem the full hydraulic model is simplified using module reduc- tion algorithm, while retaining the nonlinear characteristics of the model. Subsequently, a nonlinear programming solver CONOPT is used to solve the optimisation model. The proposed approached was tested on a large-scale WDN model provided in EPANET format. The considered WDN included complex structures and interactions between pump stations. Solving of several scenarios considering different horizons, time steps, operational constraints and topological changes demonstrated ability of the approach to automatically generate and solve optimisation problems for variety of requirements.

An optimization problem regarding water removal in the press section of a paper machine is considered in this work. The proposed model tries to minimize a cost function comprising the replacement of the felts in the press section, the cost of energy to operate the press and the cost of energy in the drying section, while satisfying the constraints of water mass balance in the process. The model is classified as a mixed-integer nonlinear program (MINLP) in which the most important decisions are: a) the sequence of paper to produce or when to produce the paper; b) the need to exchange the felts; and c) when to exchange the felts. Numerical examples are presented to illustrate the performance of the model.

9 pages, 3 figures, 4 tables, We examined the spatial dynamic of artisanal fishing fleets around five European marine protected areas (MPAs) to derive general implications for the evaluation of MPAs as fisheries management tools. The coastal MPAs studied were located off France, Malta and Spain and presented a variety of spatial designs and processes of establishment. We developed a standardized methodology to define factors influencing effort allocation and to produce fishing effort maps by merging GIS with geostatistical modelling techniques. Results revealed that in most cases the factors “distance to the no-take”, “water depth”, and “distance to the port” had a significant influence on effort allocation by the fishing fleets. Overall, we found local concentration of fishing effort around the MPA borders. Thus, neglecting the pattern of fishing effort distribution in evaluating MPA benefits, such as spillover of biomass, could hamper sound interpretation of MPAs as fisheries management tools, This manuscript is a product of the fisheries working package (WP2) of the European Commission project “Marine Protected Areas as Tools for Fisheries Management and Conservation” (EMPAFISH; Contract No. 006539). Part of the data collected for this study was also funded by the European project BIOMEX (Contract No. QLRT-2001-00891). The first author was sponsored by a research fellowship of the German Research Foundation (DFG)

Due to their large variety of applications, combinatorial optimisation problems led to a great effort to develop efficient solution techniques. But among the diversity of those optimisation methods, the choice of the relevant technique to the treatment of a given problem keeps being hard. Within the Process Engineering framework, Optimal Batch Plant Design problems provide a good support to tackle, on the one hand, two Mathematical Programming techniques – DICOPT and SBB, implemented in the GAMs software – and, on the other hand, one stochastic method, i.e. a Genetic Algorithm. Various instances of a problem, with an increasing complexity, were solved with these techniques which performances were compared : the results enable to judge their efficiency in order to propose the most appropriate method according to the studied problem instance.

The potential effect of climate change on the optimal allocation of irrigation water was investigated for a Southern Italy district. The study was carried out on 5 representative crops (grapevine, olive, sugar beet, processing tomato, asparagus), considering six simulated climate change conditions, corresponding to three 30-year periods (2011-2040; 2041-2070; 2071-2100) for two greenhouse gas emission schemes proposed by IPCC (A2 and B1), plus the current climatic condition. The framework adopted was based on: i) the modeling of crop yield response for increasing levels of water supply, under current and future climatic conditions, through a non-linear regression equation and ii) the definition of the best water allocation by means of a mathematical optimization model written in GAMS. Total irrigation water (TIW) volume was allowed to vary from a low total supply 10000 m3 to 7000000 m3, whilst a fixed surface, corresponding to that currently occupied in the studied district, was assigned to each crop. The economic return was studied in terms of Value of Production less the fixed and variable irrigation costs (VPlic). The TIW volume that maximized the VPlic of the whole district surface under the current climatic condition was 5697861 m3. The total volume was partitioned among the five crops as a function of the surface occupied: grapevine>olive>processing tomato>asparagus>sugar beet. Nevertheless, grapevine and olive received seasonal volumes corresponding only to 59 and 50% of total irrigation water requirements. On the contrary, processing tomato and asparagus received seasonal water volumes close to those fully satisfying irrigation water requirements (100% and 85% ETc). Future climatic conditions slightly differed from the current one for the expected optimal allocation. Under water shortage conditions (160000 m3) the whole irrigation water was allocated to the horticultural crops. Forecasted growing season features varied to a different extent in relation to crop and scenario considered with the more intense changes observed for A2 and olive.

Background: In this study, a general model of a hybrid off-grid energy system is developed, which can be adjusted to reflect real conditions in order to achieve economical and ecological optimisation of off-grid energy systems. Methods: Using linear programming methods in the General Algebraic Modeling System (GAMS) environment, the optimal configuration of the electrical power supply system following characteristic restrictions as well as hourly weather and demand data is found. From this model, the optimal mix of solar- and wind-based power generators combined with storage devices and a diesel generator set is formed. Results: The operation of this model was tested in two real off-grid energy systems, a cluster of villages in India and Titumate in Colombia. Both optimisation processes resulted in hybrid energy systems, utilising photovoltaics (PV), lead-acid batteries and a diesel generator as a load-balancing facility. Conclusions: With respect to small off-grid energy systems, it was found that renewable energy in combination with electrical storage devices help to reduce the cost of energy compared to stand-alone diesel generator sets. The optimal solutions strongly depend on the particular load demand curve. As both PV and wind energy benefit from energy storage, the costs of the battery can be shared and the two technologies complement each other. Finally, although the optimised capacity of the diesel generator remains nearly constant, its contribution to the total power generation is being substituted by renewable energy sources, which serve as fuel-saving technologies.